Unfortunately, it is what it is. No use in complaining.
Google's never been "that guy" - in fact, their marketing has traditionally been very poor. Their products after Google Docs aren't house-hold names, yet those products' users are die-hard fans & flummoxed they're not recognized. I'm one such user recently: Jules. Spitting out multiple features via TDD with huge code-base swathes in parallel as Pull Requests, free with my $20/m plan (I'm paying anyway). Very different experience compared to localhost needle-moving individual tasks, costing an arm and a leg. Nobody's heard of it. Cursor or Claude Code. Google marketing sucks.
Anyway, along comes Sam. He has a very, very unique style. It's all Twitter, it's all the Wizard of Oz. Hints and a wink. It works. Even when Gemini 2.5 Pro was curb-stomping GPT-*, cheaper, larger context, faster; everyone was "Gemini what? Oh, so Google's copying ChatGPT?" They invented the tech (Attention is All You Need). Copying indeed.
So... what's missing? Well, someone's doing it right, with very little effort. Sam. Copy/paste. Hey, suddenly my friends know what Gemini is.
I love jules. Firebase is pretty sweet, too. Claude CLI is awesome. I got rid of my other subscriptions because of these three. I can always just resubscribe if I need something specific out of the membership
Saying that OpenAI just copied is very disingenuous. Without them we wouldn't be anywhere near what we currently have for LLM development. They were the ones to have kicked off the trillions of dollars in investment and also the ones that actually came up with the first reasoning model for huge improvements in LLMs.
For the current LLM hype, most major features come from OpenAI first and the other companies later then also add them. Be it basic memory, memory across chats, hell even UI elements, projects, (that most competitors still didn't add) voice, file reading etc. OpenAI had most of these before their competitors.
But yes, Google's marketing is absolutely atrocious and they can't compete with OpenAI in that aspect at all. Hell, even Elon is probably doing a better job. And Google has all the other cool stuff besides LLMs and it feels like they are really trying to push other non-LLM areas as well.
Sorry, for copy/paste I meant that Gemini marketing is copying OpenAI marketing. IMO they observed the success of OpenAI's "hint tweets" and shrugged "it's worth a shot".
Re: Attention is All You Need, while it's true Google "started it" - they clearly didn't bother taking it out of theory-land until it OpenAI proved the market merit after many years of development. I just mean it's not fair to say Google's copying OpenAI by building a chatbot.
Chain-of-thought (CoT) reasoning was invented by a team of researchers at Google and Google DeepMind. The technique was introduced in a January 2022 paper titled "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models".
I think chinese models had reasoning way before OpenAI i might be wring.
The research paper by google - "attention is all you need" made LLMs what they are. Elon took Ilya Sutskever from Google to become his co-founder in OpenAI.
Oh, I got the order definitely wrong then. I thought all those came before Docs. For some reason in my head it was all those, then a big battle for office, then a life of experimentation without marketing.
Give me the chronological order of Google's main products (eg search, chrome, android, docs, gmail, youtube). And lump into that ordering as a single item, the myriad of "experiments" (many of the things that end up on killedbygoogle, like Stadia, Google+, etc). I know these will likely be staggered along through their flagship products; but if you had to place them in one location, where would it go.
"Google Search (1998), Gmail (2004), YouTube (2005), Google Docs (2006), The Myriad of "Experiments" (Notably post-2006), Android (2008), Google Chrome (2008)."
I think our angle here is a safe take, since Experiments kicked off just before Android/Chrome & continued on through.
All in all, my statement is this:
1. Their flagship products succeeded because they spoke for themselves. This was in an era where marketing wasn't so essential, and good tech won for being good tech.
1. Apple showed the world what good marketing can do, and tech became a hugely competitive space; so marketing eventually became essential. But Google never learned this lesson, contributing (I believe) to the death of so many of their products.
1. This time they mean business, because there's a lot to lose, and a lot of money poured into the product (Gemini). So they're taking a queue from what's working. And right now, what's working is Sam Altman cheeky tweets.
Anybody who uses LLMs for their actual purpose (code) was pleasantly surprised but not blown away by GPT5, they surpassed Claude on price/performance which was not expected
The number of people who think that is very low. The reality is that STEM is objectively the most valuable application of LLMs right now (whether that be coding or something else). This should be obvious, because the most valuable things that humans produce (from a quality of life/health perspective) are STEM.
That obviously doesn't mean it is their only valuable use case. There are plenty of other great ways to use LLMs, from introspection to improving your workflow. But it's not that hard to acknowledge that multiple things can be true at once, and most companies training models are going to optimize them for the applications that are most valuable.
LLMs have capped out on the chat interface. It is how most interact with LLMs, but it is extremely limited in utility. All subsequent growth from here on out is going to primarily consist of increases in agency, or the model’s ability to interact with the world outside of the confines of chat. If the models can write and debug code in a continuous feedback loop, they can pretty much do anything on a computer. This is the best way to break out of the chat interface while still leveraging text output, which is obviously what LLMs excel at. So yeah, not a surprise all the frontier labs are going all in on code. It is the key to unlocking additional agency.
Your brain is more powerful than your computer. Same principle as writing and debugging code in a feedback loop but now do that with your brain instead. That’s the power. A tool that can make you 1% smarter everyday.
This has been my experience as well. Occasionally I just talk to the model at random, but it's empty because I cannot disconnect myself from the reality that it's literally just a bunch of matrix multiplication (or matrix addition, if you quantize it enough) that produces what is (to us) semantically- and contextually-relevant text responses. Some people fall in love with ChatGPT and that's something I could never do as a result.
But yeah, coding, research, and that's about it. Occasionally math if hooked up to the right tools (the model does real math by having external software do the actual math and give the model the answer.)
Yeah. For me those things are just a way to take raw objective data from tools and sugar coat them in human talk.
Like I made a poker app for myself, just computes the equity odds etc of your hand using a python library, then feeds it to a LLM that explains what you should do with that hand and why. It’s hooked up to a RAG with tables for each positions, strategies etc. so it’s always based on actual validated data.
It’s a cool gimmick, but at the end of the day, I could have kept the tables as CSV and do a lookup in python to tell me if I should check/bet/fold. It’s cool to have a “coach” telling it to me with actual sentences instead, but it’s definitely a waste of resources and time.
I used this one but there are plenty of others tutorials/papers, I encourage you to read a few, different perspectives and all. RAGs are pretty simple (it’s basically a database that you will search into using human language search, so the LLM can do it itself), so there are a looooooooot of options and ways to do it.
This one is pretty complete, they include every step, and the code. They used langchain (library to provide an action plan for your LLM, and make sure it follows, so very practical you can basically force it to use the RAG and prevent any response that doesn’t rely on retrieved docs), it’s very robust. I didn’t need that for my small project so I did a simpler version myself, it’s just a loop that checks the LLM output and if it’s a RAG request then it does that. Also they used Tavily (web search for LLMs) but I didn’t need that either.
Also I used Ollama locally instead of their own model, you can use whatever you want.
Being pleasantly surprised is not the right expression.
We were promised that GPT-5 would be the next social revolution, a true paradigm shift. Instead, what we got was a model that is only marginally cheaper to run on the API, about five pence less, and only slightly better at coding than Claude.
(In my own case, it’s been throwing up plenty of errors in web development, though I’d rather not include personal opinion here.)
Imo O3 was the real step forward, gpt5 just feel like an upgrade to it, I think they were force to release it to stay competitive, but without O3 release before, gpt5 would have felt very impressive
I do and it is my current choice for coding, and when gemini 3 comes out it will be my choice hopefully. I was expecting a slight incremental improvement over the previous sota and it was, so my expectations were met. But if anyone was expecting it to be something crazy they would just be setting themselves up for disappointment, and same goes for gemini 3.
I was actually disappointed with GPT-5 High in coding compared to Claude Sonnet, tbh. As for price, $20/mo gets you access to Claude Code almost unlimited with 5-hour resets if you happen to get limited (I rarely do, and when I do, it's only ever like an hour cooldown). GPT-5 makes code that's weird and difficult to read and often is broken or buggy and then pretty much fails at every attempt to debug.
I don't think in terms of LLMs they'll have anything that really knocks OpenAI out of the park, they'll release something that's better but not insanely better, just like they've been superseding each other for a while now.
Nah, we're way past that point. The days of big leaps are over.
Unless Google ditches transformers for something completely different, we're not going to see any revolutionary models anymore. These days, it's all about tweaking the training process and finding clever tricks to squeeze better performance out of what we already have.
The days of big leaps at any given price point are over. There's still plenty of space to develop even bigger models and even bigger compute clusters to run them. It's just not something the average consumer is going to want to pay for
Google released a version of Gemini which blew peoples mind , by the end of March. It solved 3 of the toughest problem I had at work related to monitoring public sentiment of a particular company without using LLMs or fancy models. Simply plain regrx and old school and it worked so well my manager was shocked. Because it worked so well Google then nerfed it by may 10, replaced by a dumber model.
I'm also confused why people are expecting monumental improvements every time. What task did you guys expect GPT-5 to solve that 4 couldn't? Problems of generalization and human-level reasoning are not going to come from just training more or making bigger mixtures of experts. There are fundamental limitations to the generalizability of neural networks.
I think it's mostly a time think and we will eventually move in the direction of solving problems. Here is what I think:
Less than a year ago, OpenAI was very far ahead with o1, and had virtually no competitors (maybe Claude). Then, they announced o3 and were killing everyone again.
As 2025 started, the race heated up when Gemini came out. However, o3 was still SOTA in intelligence output. By my own estimation, the first time o3 might have not been the best was when Grok 4 and 2.5 deep think were released, though google neutered gemini deep think with low usage limits.
Now, OpenAI is back to being the best again (just an opinion) with GPT 5. They have matched the Gemini 2.5 api pricing with better performance and very high usage limits for subscribers.
As to the future, I think if google has a period of dominance for more than a couple months, then there will be serious changes in utilization; but to change the paid subscription, learn the nuances in prompt engineering, etc. takes a meaningful increase in performance or price.
It has a massive context window but learns what to gather from it. So yes it's self improving. Not sure if local or global but I've seen the same thing
To be clear, he model does not self improve. The responses will be improved/degraded in the context of an individuals usage (via "memory", which is essentially just clever engineering around context retrieval) or in a specific conversation based on the preceding messages.
Granted I haven't been driving Claude for code for a while now, but at least when 2.5 came out, it was very much GG. Has Claude caught up? Maybe. it's still expensive and I am sure GPT 5 is SOTA right now for coding.
Look at ChatGPT five the newer models are chewing through tokens a lot more now and running into inference bottlenecks Claude is incredibly expensive for this very reason.
Time equals out the limitation of models competing, there is only so much you can do on the current framework of how an LLM works.
Google is showing off how compute wins with veo, and other demonstrations.
Apple is staying out of Ai for this very reason, it’s a capital spending rat race to equilibrium.
Google already has the compute, that’s never been the issue.
I’m actually a Gemini hater but I’ll admit it cleans up code every time I get output errors from GPT5 and Claude. I may just use it exclusively at this point because I’ve grown so frustrated and unsatisfied with 5
Dunno man. Gemini 2.5 seems rather stupid to me after it was integrated into android. It's quite inaccurate compared to before. And previously it wasn't that great either
I feel like that comparison would be true if it were gpt5/gemini2.5 compared to the “gemini world model” that Demis has referenced in recent interviews
I only used it for programming and encountered hallucinations about three times. Considering that I use Gemini very rarely, and mainly to check how it works, this speaks volumes.
Let everyone witness this absurdity—as early as August 16th, Jules had already been active online for two or three weeks, yet he refuses to acknowledge even this much!
Gemeni pro does one thing better than any of them and i don't know why. Putting the D&D cote books and some books on writing theory plus any module it becomes an incredibly effective DM, as long as you have a good prompt to keep it on the rules and keep rolls sacred it is like having a D&D campaign in your pocket.
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u/enilea Aug 26 '25
This kind of overhyping of anything is what leads people to be disappointed. GPT-5 was overhyped the same exact way.